Should I use exploration strategy in Policy Gradient algorithms?
I believe that if I follow the policy (sample an action from the policy) I make use of exploration because each action has a certain probability so I will explore all actions for a given state.


Yes, having a stochastic policy function is the main way that a lot of policy gradient methods achieve exploration, including REINFORCE, A2C, A3C.


  Is it beneficial or is it common to use extra exploration strategy like UCB, Thompson sampling etc. n such algorithms?


It can be, but needs to be done carefully, as the gradient sampling for the policy function is different. Many policy gradient methods are strictly on-policy and will not work if you simply add extra exploration. It is relatively straightforward to adjust the critic part of actor-critic methods by using e.g.  Q learning update rules for it. However, the gradient of the policy function is trickier.

There are some policy gradient methods that do work with a separate, tunable, exploration function. Deep Deterministic Policy Gradient (DDPG) may be of interest to you - as per the title, it works with a deterministic policy function, and exploration is achieved by adding a separate noise function on top. The sampling for policy gradient is then corrected for being off-policy.